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Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status
BACKGROUND: Alzheimer’s disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neur...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583332/ https://www.ncbi.nlm.nih.gov/pubmed/37848950 http://dx.doi.org/10.1186/s13195-023-01281-y |
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author | Gallagher, Rigina L. Koscik, Rebecca Langhough Moody, Jason F. Vogt, Nicholas M. Adluru, Nagesh Kecskemeti, Steven R. Van Hulle, Carol A. Chin, Nathaniel A. Asthana, Sanjay Kollmorgen, Gwendlyn Suridjan, Ivonne Carlsson, Cynthia M. Johnson, Sterling C. Dean, Douglas C. Zetterberg, Henrik Blennow, Kaj Alexander, Andrew L. Bendlin, Barbara B. |
author_facet | Gallagher, Rigina L. Koscik, Rebecca Langhough Moody, Jason F. Vogt, Nicholas M. Adluru, Nagesh Kecskemeti, Steven R. Van Hulle, Carol A. Chin, Nathaniel A. Asthana, Sanjay Kollmorgen, Gwendlyn Suridjan, Ivonne Carlsson, Cynthia M. Johnson, Sterling C. Dean, Douglas C. Zetterberg, Henrik Blennow, Kaj Alexander, Andrew L. Bendlin, Barbara B. |
author_sort | Gallagher, Rigina L. |
collection | PubMed |
description | BACKGROUND: Alzheimer’s disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neuroimaging have been limited. Sensitive markers that may account for or predict cognitive dysfunction for individuals in early disease stages are critical. METHODS: Participants (n = 296) defined on A and T status and spanning the AD-clinical continuum underwent multi-shell diffusion-weighted magnetic resonance imaging to generate Neurite Orientation Dispersion and Density Imaging (NODDI) metrics, which were tested as markers of N. To better define N, we developed age- and sex-adjusted robust z-score values to quantify normal and AD-associated (abnormal) neurodegeneration in both cortical gray matter and subcortical white matter regions of interest. We used general logistic regression with receiver operating characteristic (ROC) and area under the curve (AUC) analysis to test whether NODDI metrics improved diagnostic accuracy compared to models that only relied on cerebrospinal fluid (CSF) A and T status (alone and in combination). RESULTS: Using internal robust norms, we found that NODDI metrics correlate with worsening cognitive status and that NODDI captures early, AD neurodegenerative pathology in the gray matter of cognitively unimpaired, but A/T biomarker-positive, individuals. NODDI metrics utilized together with A and T status improved diagnostic prediction accuracy of AD clinical status, compared with models using CSF A and T status alone. CONCLUSION: Using a robust norms approach, we show that abnormal AD-related neurodegeneration can be detected among cognitively unimpaired individuals. Metrics derived from diffusion-weighted imaging are potential sensitive markers of N and could be considered for trial enrichment and as outcomes in clinical trials. However, given the small sample sizes, the exploratory nature of the work must be acknowledged. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-023-01281-y. |
format | Online Article Text |
id | pubmed-10583332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105833322023-10-19 Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status Gallagher, Rigina L. Koscik, Rebecca Langhough Moody, Jason F. Vogt, Nicholas M. Adluru, Nagesh Kecskemeti, Steven R. Van Hulle, Carol A. Chin, Nathaniel A. Asthana, Sanjay Kollmorgen, Gwendlyn Suridjan, Ivonne Carlsson, Cynthia M. Johnson, Sterling C. Dean, Douglas C. Zetterberg, Henrik Blennow, Kaj Alexander, Andrew L. Bendlin, Barbara B. Alzheimers Res Ther Research BACKGROUND: Alzheimer’s disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neuroimaging have been limited. Sensitive markers that may account for or predict cognitive dysfunction for individuals in early disease stages are critical. METHODS: Participants (n = 296) defined on A and T status and spanning the AD-clinical continuum underwent multi-shell diffusion-weighted magnetic resonance imaging to generate Neurite Orientation Dispersion and Density Imaging (NODDI) metrics, which were tested as markers of N. To better define N, we developed age- and sex-adjusted robust z-score values to quantify normal and AD-associated (abnormal) neurodegeneration in both cortical gray matter and subcortical white matter regions of interest. We used general logistic regression with receiver operating characteristic (ROC) and area under the curve (AUC) analysis to test whether NODDI metrics improved diagnostic accuracy compared to models that only relied on cerebrospinal fluid (CSF) A and T status (alone and in combination). RESULTS: Using internal robust norms, we found that NODDI metrics correlate with worsening cognitive status and that NODDI captures early, AD neurodegenerative pathology in the gray matter of cognitively unimpaired, but A/T biomarker-positive, individuals. NODDI metrics utilized together with A and T status improved diagnostic prediction accuracy of AD clinical status, compared with models using CSF A and T status alone. CONCLUSION: Using a robust norms approach, we show that abnormal AD-related neurodegeneration can be detected among cognitively unimpaired individuals. Metrics derived from diffusion-weighted imaging are potential sensitive markers of N and could be considered for trial enrichment and as outcomes in clinical trials. However, given the small sample sizes, the exploratory nature of the work must be acknowledged. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-023-01281-y. BioMed Central 2023-10-17 /pmc/articles/PMC10583332/ /pubmed/37848950 http://dx.doi.org/10.1186/s13195-023-01281-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Gallagher, Rigina L. Koscik, Rebecca Langhough Moody, Jason F. Vogt, Nicholas M. Adluru, Nagesh Kecskemeti, Steven R. Van Hulle, Carol A. Chin, Nathaniel A. Asthana, Sanjay Kollmorgen, Gwendlyn Suridjan, Ivonne Carlsson, Cynthia M. Johnson, Sterling C. Dean, Douglas C. Zetterberg, Henrik Blennow, Kaj Alexander, Andrew L. Bendlin, Barbara B. Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status |
title | Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status |
title_full | Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status |
title_fullStr | Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status |
title_full_unstemmed | Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status |
title_short | Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status |
title_sort | neuroimaging of tissue microstructure as a marker of neurodegeneration in the at(n) framework: defining abnormal neurodegeneration and improving prediction of clinical status |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583332/ https://www.ncbi.nlm.nih.gov/pubmed/37848950 http://dx.doi.org/10.1186/s13195-023-01281-y |
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